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Multilayer perceptron questions

  1. Nov 16, 2015 #1
    So I am experimenting with different configurations of multilayer perceptrons in Matlab and my training data are extracted from images which I want to classify.

    -I am currently using adaptive learning with momentum backpropagation (traingdx) setting different initial learning rates.What I get is that for low values I have a pretty good results but when the initial rate gets bigger the accuracy of my model drops dramatically.How can this be explained?

    -Another question I have is how different output activation functions can affect your model.Are there some heuristics for this or just trial and error? For example I get good results with {'tansig', 'tansig', 'purelin'}, {'tansig', 'tansig', 'tansig'} but {'tansig', 'tansig', 'logsig'} fails, I suspect it has to do with negative values getting zeroed by logsig.
  2. jcsd
  3. Nov 21, 2015 #2
    Thanks for the post! This is an automated courtesy bump. Sorry you aren't generating responses at the moment. Do you have any further information, come to any new conclusions or is it possible to reword the post?
  4. Nov 22, 2015 #3


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